95 citations · 105 across the 3 of their papers we have counts for
3 papers · 1 filter
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal +2
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so…
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
Alexander Neergaard Olesen, Poul Jennum, Paul Peppard +2
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50…
A deep learning architecture to detect events in EEG signals during sleep
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal +2
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as…